Computational Discovery of Microstructured Composites with Optimized Trade-Off between Strength and Toughness

Abstract

The conflict between strength and toughness is critical to practical engineering problems. To create structural composites with extraordinary strength and toughness, previous works mainly drew inspiration from nature and attempted to replicate nacrelike structures in synthetic composites using a brick-and-mortar architecture. There is no microscale control over constituent materials and the designed composites often exhibit anisotropic properties. Recent advances in high-resolution multi-material additive manufacturing enable the creation of high-performance heterogeneous composites through voxel-level microstructure configurations. However, past efforts in exploring these designs suffered from a limited design space and a weak benchmark that only comprised base materials. More importantly, they failed to address the clear discrepancies between simulation predictions and experimental measurements (the sim-to-real gap). To our best knowledge, no work has been successfully conducted to tackle the conflict between strength and toughness while taking on the sim-to-real challenge. In this work, we propose a computational pipeline where microstructures with optimal strength and toughness trade-offs are automatically discovered and analyzed for intrinsic toughening mechanisms. Based on a fast physics-based simulator, it employs a competitive game approach to bridging the gap between simulation and experiment. The pipeline will open the door for reversing the traditional scientific discovery process through analysis-by-synthesis, and potentially generalize to a wide range of applications in material science, chemistry, pharmaceutics, robotics, etc.